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about adapter noise cancellation

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alfaria

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how the adapter noise cancellation work....
explain...how the operation of dsp in adaptive noise cancellation
 

1)how to convert signal from analog to digital signal using digital signal processor
 

Hi

Adaptive signal processing can be considered to be a process in which the parameters used for the processing of signals changes according to some criterion. Usually the criterion is the estimated mean squared error or the correlation. Adaptive processing usually refers to adaptive filtering; that is, filtering in which the parameters of the filter can change with the independent variable (usually space or time). When data can be re-played (not real time), one can iterate the adaptation process to optimize the convergence process. It is pertinent at this stage to introduce a glossary of terms, which should be understood by the student:

Adaptive, adaptation - a process in which the parameters used for the processing of signals changes according to some criterion.

Adaptive filter - the adaptive process is applied to a filter; i.e., the values of filter parameters can change over the course of the independent variable (usually time or space) based on an error criterion.

Convergence - the weights for adaptation arrive at the optimized value.

Convergence Coefficients - the value of the parameter(s) that determine the speed of convergence of the adaptive weights toward the optimal weights.

Correlation - a measure of the similarity between two signals ranging from -1 to 1 when normalized. It can also be used as an error function (correlation coefficient closer to 0 means signals are more dissimilar).

Error - for signal processing, the difference between two signals. The less the error, the greater the similarity of the two signals.

Error criterion (function) - the error equation or equations used to optimize a set of weights. Often, the mean squared error estimate is used.

Locus - a range in weights centered around the optimal weight.

Incomplete convergence - the weights approach but do not reach the optimal values. This may occur because the convergence coefficient is too large, whereby the weights oscillate around the area of the locus. It may also happen because the convergence coefficient is too small, whereupon the weights stop converging before reaching the optimal value.

Optimal, optimality - implies that a set of weights associated with certain signal parameters are adjusted to minimize the error.

Optimal weights, weighting - the values of the weights when they have converged, i.e., when the error function is at a minimum.

Performance index - the error criterion or criteria used to adjust the values of the weights.

Performance surface - the relationship between the error function and the weighted parameter(s). Often this surface is concave and at the minimum value is the optimized weighting.

Weight - the value of a parameter used to adjust the signal shape. And example is the gain of the signal.

Weighting function - the set of parameter values used to adjust the signal shape.


for further info go to

**broken link removed**




hope it helped u



-helios

Added after 5 minutes:

Sampling is not done in the DSP processor core but it is done by a device called a sampler .... since it samples the input and converts it to digital( 0 's & 1's ) its called as a ADC( Analog to digital converter ).


so each DSP processor is interfaced with a ADC and the input are feed throu ADc channels and the ADC samples /digitizes it and send to DSP pro for processing.

the sampling frequency of ADC is programmable the user can change the sampling rate at any point form the master program .




post more questions if u dont undersnat things

hope it helped u

-helios
 

I will recommend you to read the "Adaptive Filter Theory by Simon Haykens".


-Best Regards
 

1)How noise cancellation operate in digital signal processing.
2)Diffrent between adapter and active noise cancellation
 

Noise cancellation is based on the basic idea of frequency elimination and initially band reject filters were used to remove the noise from a signal then after that more robust filters came...................... some depend on the feed back mechanism....

Adaptive noise cancellation takes the consideration that the amount of noise and type of noise is unkown to the system so in the first step the system is estimated ...after the system is estimated the correction is applied accordingly....

For further details you can read
Adaptive Filters Book or any tutorial on adaptive signal processing where the random signal is the input and system is unknown..

-Regards
 

Adaptive Noise cancellation is a two microphone noise cancellation, in which one microphone collect SIGNAL+NOISE and other acquire a sample on noise only signal. Then by means of LMS algorithm a filtered version of noise subtract from SIGNAL+Noise and a clean signal will produce.

See book of Dr. Vaseghi for LMS application in speecg processing
 

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